AN APPROACH TO THE EXTRACTION OF REMOTELY-SENSED INFORMATION OF<br />
FOREST COVERAGE BASED ON PCA/NDVI

doi:10.6046/gtzyyg.2007.02.21

Download: PDF(429 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

With the Daxinganling area as the experimental site, the authors used the PCA (Principal Components Analysis) to get the first weight and the results of the NDVI (Normalized Difference Vegetation Index), and employed a certain band in the LandSat TM1 to 7 to realize the combination of the wave band, which could enhance the contrast between the forest information and the background information. The study also used the method of maximum likelihood to realize the supervised classification of the images, whose accuracy could exceed 92%. This paper presents two experimental investigation images with different quantities of cloud and different extents of forest coverage. This investigation shows that this method can improve the automation and precision in the extraction of the forest coverage information.

Keywords Buried coal field TM image Information extraction Geological effect

TP79：S771．8　

Issue Date: 24 July 2009

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

DENG Shu-Bin, WU Hong-Gan, JIANG Tao. AN APPROACH TO THE EXTRACTION OF REMOTELY-SENSED INFORMATION OF
FOREST COVERAGE BASED ON PCA/NDVI[J]. REMOTE SENSING FOR LAND & RESOURCES,2007, 19(2): 82-85.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2007.02.21 OR https://www.gtzyyg.com/EN/Y2007/V19/I2/82

[1]	WEI Yingjuan, LIU Huan. Remote sensing-based mineralized alteration information extraction and prospecting prediction of the Beiya gold deposit, Yunnan Province[J]. Remote Sensing for Natural Resources, 2021, 33(3): 156-163.
[2]	MOU Xiaoli, LI He, HUANG Chong, LIU Qingsheng, LIU Gaohuan. Application progress of Google Earth Engine in land use and land cover remote sensing information extraction[J]. Remote Sensing for Land & Resources, 2021, 33(2): 1-10.
[3]	XIA Yan, HUANG Liang, CHEN Pengdi. Tobacco fine extraction from UAV image based on fuzzy-superpixel segmentation algorithm[J]. Remote Sensing for Land & Resources, 2021, 33(1): 115-122.
[4]	TIAN Lei, FU Wenxue, SUN Yanwu, JING Linhai, QIU Yubao, LI Xinwu. Research on spatial change of the boreal forest cover in Siberia over the past 30 years based on TM images[J]. Remote Sensing for Land & Resources, 2021, 33(1): 214-220.
[5]	WANG Lin, XIE Hongbo, WEN Guangchao, YANG Yunhang. A study on water information extraction method of cyanobacteria lake based on Landsat8[J]. Remote Sensing for Land & Resources, 2020, 32(4): 130-136.
[6]	Chang LIU, Kang YANG, Liang CHENG, Manchun LI, Ziyan GUO. Comparison of Landsat8 impervious surface extraction methods[J]. Remote Sensing for Land & Resources, 2019, 31(3): 148-156.
[7]	Zhan YIN, Lijun ZHANG, Jianliang DUAN, Pei ZHANG. Improvement and application of forced invariance vegetation suppression in southern vegetation area[J]. Remote Sensing for Land & Resources, 2019, 31(2): 82-88.
[8]	Yueru WANG, Pengpeng HAN, Shujing GUAN, Yu HAN, Lin YI, Tinggang ZHOU, Jinsong CHEN. Information extraction of Dracaena sanderiana planting area based on Landsat8 OLI data[J]. Remote Sensing for Land & Resources, 2019, 31(1): 133-140.
[9]	Yong WANG, Yinling ZHANG, Shucheng YOU, Zhongwu WANG, Hai WEI, Yang LI. Checkup of land consolidation project using ZY1-02C data[J]. Remote Sensing for Land & Resources, 2018, 30(1): 144-149.
[10]	XUE Qing, WU Wei, LI Mingsong, DONG Shuangfa, ZHANG Xinyi, SHI Haigang. Application of GF-1 satellite data to remote sensing monitoring of the mine[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(s1): 67-72.
[11]	DIAO Shujuan, LIU Chunling, ZHANG Tao, HE Peng, GUO Zhaocheng, TU Jienan. Extraction of remote sensing information for lake salinity level based on SVM: A case from Badain Jaran desert in Inner Mongolia[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 114-118.
[12]	HU Yong, ZHANG Xiaocheng, MA Zezhong, ZHOU Zhiyue. Rural residential area extraction from UAV remote sensing imagery[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 96-101.
[13]	ZHANG Bing, CUI Ximin, WEI Rui, SONG Baoping, ZHAO Xuyang. Classification and information extraction of plateau landform based on IRS-P6 satellite image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 120-125.
[14]	LU Dingge, WU Hong, GUO Qi, CHEN Mengjie. Feature extraction and analysis of the Lijiang River water system form based on the Google Earth image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 161-167.
[15]	DUAN Qiuya, MENG Lingkui, FAN Zhiwei, HU Weiguo, XIE Wenjun. Applicability of the water information extraction method based on GF-1 image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(4): 79-84.